基于增强随机森林算法的COVID-19潜伏期估计

P. Rathnayake, J. M. D. Senanayake, D. Wickramaarachchi
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引用次数: 0

摘要

冠状病毒病于2019年12月首次发现。截至2021年7月,在这种传染病开始以来的19个月内,已报告了超过1.8亿例病例。严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)病毒的潜伏期可以定义为接触病毒和出现症状之间的一段时间。大多数受影响的病例在此期间无症状,但他们可以将病毒传播给他人。潜伏期是决定检疫或隔离期的一个重要因素。根据目前的研究,SARS-CoV-2的潜伏期为2至14天。由于存在一个范围,因此难以确定疑似病例的具体潜伏期。因此,所有疑似病例都应经历14天的隔离期,这可能导致不必要的资源分配。本研究的主要目的是在确定影响潜伏期的因素后,利用机器学习技术开发一个分类模型,对潜伏期进行分类。本研究使用了5-80岁年龄组的患者记录。该数据集由来自中国、日本、韩国和美国等不同国家的500例患者记录组成。本研究发现,患者的年龄、免疫功能状态、性别、与患者的直接/间接接触以及居住地点对潜伏期有影响。本研究比较了几种监督学习分类算法,以寻找表现最佳的孵化类分类算法。每个孵化班的加权平均值用于评估模型的整体性能。随机森林算法在分类孵化类方面优于其他算法,准确率为0.78,召回率为0.84,f1得分为0.80。采用AdaBoost算法对模型进行微调。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of the incubation period of COVID-19 using boosted random forest algorithm
Coronavirus disease was first discovered in December 2019. As of July 2021, within nineteen months since this infectious disease started, more than one hundred and eighty million cases have been reported. The incubation period of the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be defined as the period between exposure to the virus and symptom onset. Most of the affected cases are asymptomatic during this period, but they can transmit the virus to others. The incubation period is an important factor in deciding quarantine or isolation periods. According to current studies, the incubation period of SARS-CoV-2 ranges from2 to 14 days. Since there is a range, it is difficult to identify a specific incubation period for suspected cases. Therefore, all suspected cases should undergo an isolation period of 14 days, and it may lead to unnecessarily allocation of resources. The main objective of this research is to develop a classification model to classify the incubation period using machine learning techniques after identifying the factors affecting the incubation period. Patient records within the age group 5–80 years were used in this study. The dataset consists of 500 patient records from various countries such as China, Japan, South Korea and the USA. This study identified that the patients' age, immunocompetent state, gender, direct/indirect contact with the affected patients and the residing location affect the incubation period. Several supervised learning classification algorithms were compared in this study to find the best performing algorithm to classify the incubation classes. The weighted average of each incubation class was used to evaluate the overall model performance. The random forest algorithm outperformed other algorithms achieving 0.78 precision, 0.84 recall, and 0.80 F1-score in classifying the incubation classes. To fine-tune the model AdaBoost algorithm was used.
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